Statistical Analysis of Particulate Matter Over an Urban Area, India

Abstract

In recent years India is developing rapidly and this development has serious impact on our environment. The rapid growth in air pollution events is becoming more and more serious. This study aims to explore the impact of meteorological factors on PM_2.5 and PM₁₀ concentrations in Delhi, India. Delhi is an appropriate place for study about long range transport pollutants and the correlation between particulate concentrations and meteorological conditions as it is located in the northern part of India is capital of our country and has been affected by pollutants from within the city and outside the city areas.The particulate matter concentrations and meteorology data from April, 2018 to November, 2021 were collected. There is a significant correlation between the explanatory variables and to remove the multi-collinearity and to determine independent explanatory variables we have used Principal Component Analysis (PCA). Using the component scores obtained from PCA, we have done the regression analysis for prediction of PM_2.5 and PM₁₀. To analyze the seasonal variation, we separated the groups for analysis by creating a grouping variable. There was a statistically significant difference between groups as determined by one-way ANOVA. A Tukey post hoc test revealed that there was a significant difference in the average values of PM concentrations (PM_2.5 and PM₁₀) between most of all the pairs except the season’s summer and Post Monsoon.